Big data is also referred to as 3-v data, where the 3-v represents volume, velocity, and variety. With the rapid development of data storage and cloud computing facilities, volume and velocity are no longer the bottlenecks of big data application. Variety poses more challenges. The data that we obtained may come from extremely heterogeneous sources. To fuse the data together, data fusion can be performed at three different levels: data level, feature level, and decision level. Compared with the feature level fusion and decision level fusion, data level fusion preserves more original information and is more attractive for information extraction. Alternatively, feature level fusion can discover new patterns and form new insights and consequently is often used for information integration. When systematic decisions need to be made, we need decision fusion methods to focus on the valuable information that could bring out optimal decisions. In this talk, we will introduce a set of statistical tools for data fusion. We first talk about B-scaling and F-scaling algorithms for multimodal data level fusion. Then, we show a decentralized computing algorithm for feature level fusion for multisite/multisensor data. Finally, we present a diversity driven method for decision level fusion.